{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/contrib_utils.py:305: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/contrib_utils.py:310: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TensorFlow version 1.15.3 has been patched using tfdeterminism version 0.3.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from collections import OrderedDict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_style('ticks')\n",
    "import toml\n",
    "from matplotlib import colors\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "import pandas as pd\n",
    "\n",
    "from modules.utils import load\n",
    "from tasks.totaling import TotalizeCleansingWrtEval\n",
    "from modules.plot import plot_filling_std, merge_line_legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output dir\n",
    "out_dir = './results'\n",
    "os.makedirs(out_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visual settings\n",
    "legend_metrics = [\n",
    "    'log_likelihood_kde',\n",
    "    'log_inception_score',\n",
    "    'fid',\n",
    "    'if',\n",
    "    'loss_d',\n",
    "    'random',\n",
    "    'metric_no_removal',\n",
    "]\n",
    "\n",
    "d_metric_rename_fig = dict((\n",
    "    ('fid', 'Influence on FID (Ours)'),\n",
    "    ('inception_score', 'Influence on IS (Ours)'),\n",
    "    ('log_inception_score', 'Influence on IS (Ours)'),\n",
    "    ('loss_d', 'Influence on Disc. Loss'),\n",
    "    ('random', 'Random'),\n",
    "    ('if', 'Isolation Forest'),\n",
    "    ('metric_no_removal', 'No Removal'),\n",
    "    ('if_data', 'Isolation Forest'),\n",
    "    ('log_likelihood_kde', 'Influence on ALL (Ours)')\n",
    "))\n",
    "\n",
    "d_metric_rename_table = dict((\n",
    "    ('fid', 'Influence on FID'),\n",
    "    ('inception_score', 'Influence on IS'),\n",
    "    ('log_inception_score', 'Influence on IS'),\n",
    "    ('loss_d', 'Influence on D loss'),\n",
    "    ('random', 'Random'),\n",
    "    ('if', 'Isolation Forest'),\n",
    "    ('metric_no_removal', 'No Removal'),\n",
    "    ('if_data', 'Isolation Forest'),\n",
    "    ('log_likelihood_kde', 'Influence on ALL')\n",
    "))\n",
    "\n",
    "d_metric_color = dict((\n",
    "    ('fid', 'C0'),\n",
    "    ('inception_score', 'C1'),\n",
    "    ('log_inception_score', 'C1'),\n",
    "    ('loss_d', 'C4'),\n",
    "    ('random', 'C5'),\n",
    "    ('if', 'C3'),\n",
    "    ('metric_no_removal', 'grey'),\n",
    "    ('if_data', 'C3'),\n",
    "    ('log_likelihood_kde', 'C2'),\n",
    "))\n",
    "\n",
    "d_metric_marker = dict((\n",
    "    ('fid', 'o'),\n",
    "    ('inception_score', 'D'),\n",
    "    ('log_inception_score', 'D'),\n",
    "    ('loss_d', '>'),\n",
    "    ('random', 'v'),\n",
    "    ('if', '^'),\n",
    "    ('metric_no_removal', ''),\n",
    "    ('if_data', '^'),\n",
    "    ('log_likelihood_kde', 's'),\n",
    "))\n",
    "\n",
    "d_metric_style = dict((\n",
    "    ('fid', '-'),\n",
    "    ('inception_score', '-'),\n",
    "    ('log_inception_score', '-'),\n",
    "    ('loss_d', '-'),\n",
    "    ('random', '-'),\n",
    "    ('if', '-'),\n",
    "    ('metric_no_removal', '--'),\n",
    "    ('if_data', '-'),\n",
    "    ('log_likelihood_kde', '-'),\n",
    "))\n",
    "\n",
    "\n",
    "evalmetrics = [\n",
    "    'fid',\n",
    "    'inception_score',\n",
    "    'log_likelihood_kde',\n",
    "]\n",
    "\n",
    "d_evalmetric_rename_table = dict((\n",
    "    ('fid', 'FID'),\n",
    "    ('inception_score', 'IS'),\n",
    "    ('log_likelihood_kde', 'Average log-likelihood'),\n",
    "))\n",
    "\n",
    "d_evalmetric_rename_fig = dict((\n",
    "    ('fid', 'Fréchet Inception Distance'),\n",
    "    ('inception_score', 'Inception Score'),\n",
    "    ('log_likelihood_kde', 'Average Log Likelihood'),\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load results and parameters\n",
    "# these results can be loaded only after finishing the experiment command written in \"Experiment 2: Data Cleansing\" of README.md\n",
    "\n",
    "conf_2d = toml.load(open('conf/2d_cleansing.toml', 'r'))\n",
    "conf_mnist = toml.load(open('conf/mnist_cleansing.toml', 'r'))\n",
    "task_2d = TotalizeCleansingWrtEval(**conf_2d[TotalizeCleansingWrtEval.__name__])\n",
    "task_mnist = TotalizeCleansingWrtEval(**conf_mnist[TotalizeCleansingWrtEval.__name__])\n",
    "\n",
    "dir_mnist = task_mnist.output().path\n",
    "dir_2d = task_2d.output().path\n",
    "\n",
    "d_evalmetric_pvals = dict((\n",
    "    ('fid', load(os.path.join(dir_mnist, 'fid_ps_dict.pkl'))),\n",
    "    ('inception_score', load(os.path.join(dir_mnist, 'inception_score_ps_dict.pkl'))),\n",
    "    ('log_likelihood_kde', load(os.path.join(dir_2d, 'log_likelihood_kde_ps_dict.pkl')))\n",
    "))\n",
    "\n",
    "d_evalmetric_results = dict((\n",
    "    ('fid', load(os.path.join(dir_mnist, 'fid_result_dic.pkl'))),\n",
    "    ('inception_score', load(os.path.join(dir_mnist, 'inception_score_result_dic.pkl'))),\n",
    "    ('log_likelihood_kde', load(os.path.join(dir_2d, 'log_likelihood_kde_result_dic.pkl')))\n",
    "))\n",
    "\n",
    "d_evalmetric_nsamp = dict((\n",
    "    ('fid', conf_mnist['TotalizeCleansingWrtEval']['s_tr']),\n",
    "    ('inception_score', conf_mnist['TotalizeCleansingWrtEval']['s_tr']),\n",
    "    ('log_likelihood_kde', conf_2d['TotalizeCleansingWrtEval']['s_tr'])\n",
    "))\n",
    "\n",
    "d_evalmetric_removal_rates = dict((\n",
    "    ('fid', conf_mnist['TotalizeCleansingWrtEval']['removal_rates']),\n",
    "    ('inception_score', conf_mnist['TotalizeCleansingWrtEval']['removal_rates']),\n",
    "    ('log_likelihood_kde', conf_2d['TotalizeCleansingWrtEval']['removal_rates'])\n",
    "))\n",
    "\n",
    "d_evalmetric_better_sign = {\n",
    "    'fid': '+',\n",
    "    'inception_score': '-',\n",
    "    'log_likelihood_kde': '+'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mylogfmt(x,pos):\n",
    "    logx = np.log10(x) # to get the exponent\n",
    "    return u\"$10^{{{:.0f}}}$\".format(logx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figures\n",
    "assert d_evalmetric_pvals.keys() == d_evalmetric_results.keys()\n",
    "d_evalmetric_figs = {}\n",
    "for evalmetric in evalmetrics:\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    xs = d_evalmetric_nsamp[evalmetric] * np.asarray(d_evalmetric_removal_rates[evalmetric])\n",
    "    plot_filling_std(ax=ax,\n",
    "                     xs=xs,\n",
    "                     plots=d_evalmetric_results[evalmetric],\n",
    "                     xlabel='# of instances removed $\\it{n_{h}}$',\n",
    "                     ylabel=d_evalmetric_rename_fig[evalmetric],\n",
    "                     log_scale=True,\n",
    "                     keys_ignored_by_autoscale='if',\n",
    "                     loc_legend='lower left',\n",
    "                     fill=False)\n",
    "    fig.subplots_adjust(bottom=0.2)\n",
    "\n",
    "    ax.xaxis.set_major_formatter(ticker.FuncFormatter(mylogfmt))\n",
    "    ax.xaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
    "    ax.get_legend().remove()\n",
    "\n",
    "    for line in ax.lines:\n",
    "        label = line.get_label()\n",
    "        if label in d_metric_color:\n",
    "            ydata = line.get_ydata()\n",
    "            xdata = line.get_xdata()\n",
    "            line.set_color(colors.to_rgba(d_metric_color[label]))\n",
    "            line.set_label(d_metric_rename_fig[label])\n",
    "            line.set_marker(d_metric_marker[label])\n",
    "            line.set_linestyle(d_metric_style[label])\n",
    "\n",
    "    d_evalmetric_figs[evalmetric] = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.880499483655509, -2.862777021199648)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adjust plot limits\n",
    "ylim = d_evalmetric_figs['log_likelihood_kde'].axes[0].get_ylim()\n",
    "ylim_ = (ylim[0] + 0.007, ylim[1])\n",
    "d_evalmetric_figs['log_likelihood_kde'].axes[0].set_ylim(ylim_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save as pdf\n",
    "for evalmetric, fig in d_evalmetric_figs.items():\n",
    "    fig.savefig(os.path.join(out_dir, f'{evalmetric}_clean.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect lines in figs to make one legend figure\n",
    "merge_line_legends(figs=d_evalmetric_figs.values(),\n",
    "                   line_names=[d_metric_rename_fig[x] for x in legend_metrics],\n",
    "                   out_path=os.path.join(out_dir, 'legend.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lllllllllll}\n",
      "\\toprule\n",
      "{} &                     0.5k &                     1.0k &                              2.5k &                              5.0k &                             10.0k &                             15.0k &                             20.0k &                             25.0k &                             35.0k &                             45.0k \\\\\n",
      "\\midrule\n",
      "Influence on FID    &  \\thead{-0.10 \\\\ (0.13)} &  \\thead{-0.13 \\\\ (0.18)} &           \\thead{-0.18 \\\\ (0.28)} &           \\thead{-0.19 \\\\ (0.46)} &           \\thead{-0.25 \\\\ (0.45)} &           \\thead{-0.36 \\\\ (0.35)} &           \\thead{-0.38 \\\\ (0.36)} &           \\thead{-0.38 \\\\ (0.37)} &           \\thead{-0.23 \\\\ (0.46)} &           \\thead{+0.23 \\\\ (0.60)} \\\\\n",
      "Influence on IS     &  \\thead{-0.07 \\\\ (0.10)} &  \\thead{-0.10 \\\\ (0.14)} &           \\thead{-0.14 \\\\ (0.22)} &           \\thead{-0.14 \\\\ (0.37)} &           \\thead{-0.26 \\\\ (0.28)} &           \\thead{-0.32 \\\\ (0.29)} &           \\thead{-0.34 \\\\ (0.30)} &           \\thead{-0.36 \\\\ (0.30)} &           \\thead{-0.22 \\\\ (0.45)} &           \\thead{+0.17 \\\\ (0.50)} \\\\\n",
      "Influence on D loss &  \\thead{-0.03 \\\\ (0.06)} &  \\thead{-0.04 \\\\ (0.08)} &           \\thead{-0.07 \\\\ (0.07)} &           \\thead{-0.13 \\\\ (0.10)} &           \\thead{-0.18 \\\\ (0.12)} &           \\thead{-0.20 \\\\ (0.13)} &           \\thead{-0.19 \\\\ (0.14)} &           \\thead{-0.15 \\\\ (0.14)} &           \\thead{-0.06 \\\\ (0.12)} &  \\textbf{\\thead{+0.34 \\\\ (0.19)}} \\\\\n",
      "Isolation Forest    &  \\thead{+0.01 \\\\ (0.03)} &  \\thead{+0.02 \\\\ (0.03)} &  \\textbf{\\thead{+0.05 \\\\ (0.06)}} &  \\textbf{\\thead{+0.10 \\\\ (0.08)}} &  \\textbf{\\thead{+0.24 \\\\ (0.15)}} &  \\textbf{\\thead{+0.42 \\\\ (0.22)}} &  \\textbf{\\thead{+0.73 \\\\ (0.37)}} &  \\textbf{\\thead{+1.09 \\\\ (0.54)}} &  \\textbf{\\thead{+2.56 \\\\ (0.85)}} &  \\textbf{\\thead{+6.99 \\\\ (3.57)}} \\\\\n",
      "Random              &  \\thead{-0.02 \\\\ (0.04)} &  \\thead{-0.01 \\\\ (0.03)} &           \\thead{-0.01 \\\\ (0.03)} &           \\thead{-0.02 \\\\ (0.06)} &           \\thead{-0.01 \\\\ (0.07)} &           \\thead{+0.00 \\\\ (0.07)} &           \\thead{-0.00 \\\\ (0.09)} &           \\thead{-0.02 \\\\ (0.13)} &           \\thead{-0.08 \\\\ (0.15)} &           \\thead{-0.14 \\\\ (0.24)} \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\\begin{tabular}{lllllllllll}\n",
      "\\toprule\n",
      "{} &                     0.5k &                     1.0k &                     2.5k &                     5.0k &                    10.0k &                             15.0k &                             20.0k &                             25.0k &                             35.0k &                             45.0k \\\\\n",
      "\\midrule\n",
      "Influence on FID    &  \\thead{+0.03 \\\\ (0.07)} &  \\thead{+0.04 \\\\ (0.09)} &  \\thead{+0.04 \\\\ (0.17)} &  \\thead{+0.03 \\\\ (0.25)} &  \\thead{+0.04 \\\\ (0.24)} &           \\thead{+0.09 \\\\ (0.13)} &           \\thead{+0.10 \\\\ (0.12)} &           \\thead{+0.10 \\\\ (0.13)} &           \\thead{+0.04 \\\\ (0.17)} &  \\textbf{\\thead{-0.18 \\\\ (0.28)}} \\\\\n",
      "Influence on IS     &  \\thead{+0.04 \\\\ (0.05)} &  \\thead{+0.04 \\\\ (0.08)} &  \\thead{+0.05 \\\\ (0.14)} &  \\thead{+0.04 \\\\ (0.23)} &  \\thead{+0.08 \\\\ (0.15)} &           \\thead{+0.11 \\\\ (0.13)} &           \\thead{+0.12 \\\\ (0.14)} &           \\thead{+0.14 \\\\ (0.14)} &           \\thead{+0.09 \\\\ (0.25)} &           \\thead{-0.07 \\\\ (0.24)} \\\\\n",
      "Influence on D loss &  \\thead{+0.01 \\\\ (0.03)} &  \\thead{+0.01 \\\\ (0.05)} &  \\thead{+0.02 \\\\ (0.03)} &  \\thead{+0.04 \\\\ (0.04)} &  \\thead{+0.04 \\\\ (0.05)} &           \\thead{+0.04 \\\\ (0.06)} &           \\thead{+0.04 \\\\ (0.06)} &           \\thead{+0.01 \\\\ (0.06)} &           \\thead{+0.00 \\\\ (0.07)} &  \\textbf{\\thead{-0.15 \\\\ (0.11)}} \\\\\n",
      "Isolation Forest    &  \\thead{+0.00 \\\\ (0.02)} &  \\thead{+0.01 \\\\ (0.02)} &  \\thead{+0.01 \\\\ (0.04)} &  \\thead{+0.00 \\\\ (0.05)} &  \\thead{-0.01 \\\\ (0.06)} &  \\textbf{\\thead{-0.05 \\\\ (0.08)}} &  \\textbf{\\thead{-0.13 \\\\ (0.13)}} &  \\textbf{\\thead{-0.23 \\\\ (0.18)}} &  \\textbf{\\thead{-0.67 \\\\ (0.33)}} &  \\textbf{\\thead{-1.70 \\\\ (0.75)}} \\\\\n",
      "Random              &  \\thead{+0.01 \\\\ (0.02)} &  \\thead{+0.00 \\\\ (0.02)} &  \\thead{+0.01 \\\\ (0.02)} &  \\thead{+0.01 \\\\ (0.03)} &  \\thead{+0.00 \\\\ (0.05)} &           \\thead{+0.00 \\\\ (0.05)} &           \\thead{+0.00 \\\\ (0.06)} &           \\thead{+0.00 \\\\ (0.08)} &           \\thead{+0.01 \\\\ (0.09)} &           \\thead{+0.01 \\\\ (0.13)} \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\\begin{tabular}{lllllllllll}\n",
      "\\toprule\n",
      "{} &                              0.5k &                              1.0k &                              2.5k &                              5.0k &                              7.5k &                             10.0k &                             12.5k &                    15.0k &                    17.5k &                    20.0k \\\\\n",
      "\\midrule\n",
      "Influence on ALL    &  \\textbf{\\thead{+0.08 \\\\ (0.06)}} &  \\textbf{\\thead{+0.15 \\\\ (0.11)}} &  \\textbf{\\thead{+0.30 \\\\ (0.26)}} &  \\textbf{\\thead{+0.40 \\\\ (0.49)}} &           \\thead{+0.34 \\\\ (0.74)} &           \\thead{+0.13 \\\\ (1.00)} &           \\thead{-0.21 \\\\ (1.30)} &  \\thead{-0.67 \\\\ (1.63)} &  \\thead{-1.23 \\\\ (1.99)} &  \\thead{-1.85 \\\\ (2.37)} \\\\\n",
      "Influence on D loss &  \\textbf{\\thead{+0.02 \\\\ (0.02)}} &  \\textbf{\\thead{+0.04 \\\\ (0.04)}} &  \\textbf{\\thead{+0.09 \\\\ (0.09)}} &  \\textbf{\\thead{+0.17 \\\\ (0.18)}} &  \\textbf{\\thead{+0.24 \\\\ (0.28)}} &  \\textbf{\\thead{+0.29 \\\\ (0.39)}} &  \\textbf{\\thead{+0.31 \\\\ (0.52)}} &  \\thead{+0.31 \\\\ (0.66)} &  \\thead{+0.27 \\\\ (0.81)} &  \\thead{+0.20 \\\\ (0.97)} \\\\\n",
      "Isolation Forest    &           \\thead{+0.02 \\\\ (0.06)} &           \\thead{+0.04 \\\\ (0.11)} &           \\thead{+0.08 \\\\ (0.28)} &           \\thead{+0.11 \\\\ (0.56)} &           \\thead{+0.09 \\\\ (0.84)} &           \\thead{+0.02 \\\\ (1.15)} &           \\thead{-0.08 \\\\ (1.46)} &  \\thead{-0.21 \\\\ (1.75)} &  \\thead{-0.40 \\\\ (2.09)} &  \\thead{-0.66 \\\\ (2.45)} \\\\\n",
      "Random              &           \\thead{+0.01 \\\\ (0.04)} &           \\thead{+0.02 \\\\ (0.08)} &           \\thead{+0.04 \\\\ (0.19)} &           \\thead{+0.06 \\\\ (0.39)} &           \\thead{+0.06 \\\\ (0.61)} &           \\thead{+0.04 \\\\ (0.85)} &           \\thead{-0.01 \\\\ (1.09)} &  \\thead{-0.09 \\\\ (1.35)} &  \\thead{-0.20 \\\\ (1.64)} &  \\thead{-0.36 \\\\ (1.94)} \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# gen tables with p-values for latex\n",
    "xdatas = []\n",
    "for evalmetric in evalmetrics:\n",
    "    txts_with_bold = OrderedDict()\n",
    "    ns = [f'{x:.1f}k' for x in np.array(d_evalmetric_figs[evalmetric].axes[0].lines[0].get_xdata()/1000)]\n",
    "    xdatas.append(ns)\n",
    "    result = d_evalmetric_results[evalmetric]\n",
    "    no_removal_vals = np.asarray(result.pop('metric_no_removal'))\n",
    "    for metric, eval_metric_vals in result.items():\n",
    "        ps = np.asarray(d_evalmetric_pvals[evalmetric][metric])\n",
    "        means, stds = np.mean(eval_metric_vals-no_removal_vals, axis=0), np.std(eval_metric_vals-no_removal_vals, axis=0)\n",
    "        if evalmetric == 'log_likelihood_kde':\n",
    "            txts = [f'\\thead{{{mean*100:+.2f} \\\\\\\\ ({std*100:.2f})}}' for no_removal_val, mean, std in zip(no_removal_vals, means, stds)]\n",
    "        else:\n",
    "            txts = [f'\\thead{{{mean:+.2f} \\\\\\\\ ({std:.2f})}}' for no_removal_val, mean, std in zip(no_removal_vals, means, stds)]\n",
    "\n",
    "        txts_with_bold[d_metric_rename_table[metric]] = [f'\\textbf{{{txt}}}' if p < 0.05 and d_evalmetric_better_sign[evalmetric] in txt else txt for txt, p in zip(txts, ps)]\n",
    "\n",
    "    df = pd.DataFrame(txts_with_bold, index=ns)\n",
    "    # path_latex = os.path.join(out_dir, '{}_{}.txt'.format(val, metric))\n",
    "    print(df.T.to_latex(escape=False))"
   ]
  }
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